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Photograph of Leah Ding

Leah Ding Associate Professor Computer Science

Leah Ding
CAS | Computer Science
Don Myers Technology and Innovation Building 108F
PhD, University at Buffalo, 2013.

Dr. Ding is broadly interested in trustworthy machine learning with its applications in cybersecurity and scientific data analytics.

She has extensive experience doing cybersecurity R&D in industrial research labs. Before joining AU, she was a Research Principal at Accenture Labs (the R&D division of Accenture), and an adjunct professor at Johns Hopkins University.
For the Media
To request an interview for a news story, call AU Communications at 202-885-5950 or submit a request.


Spring 2024

  • CSC-316 Computer Science III

Fall 2024

  • CSC-316 Computer Science III

  • CSC-480 Introduction to Data Mining

Scholarly, Creative & Professional Activities

Selected Publications

"Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models," R. Duan, Z. Qu, L. Ding, Y. Liu, Z. Lu, accepted, to appear in the Network and Distributed System Security Symposium (NDSS), 2024.

"Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction," Y. Wang, J. Gong, D. L. Wu, L. Ding, in IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 61, pp. 1-14, 2023.

"Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data," L. Ding, R. Corizzo, C. Bellinger C, N. Ching, S. Login, R. Yepez-Lopez, J. Gong, D. L. Wu, IEEE International Conference on Big Data (2022 Big Data), Osaka, Japan, Dec. 2022, pp. 5902-5909.

"Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception," R. Duan, Z. Qu, S. Zhao, L. Ding, Y. Liu, Z. Lu, the ACM Conference on Computer and Communications Security (CCS) (ACM CCS 2022), Nov. 2022.

"A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements," S. Das, Y. Wang, J. Gong, L. Ding, S. J. Munchak, C. Wang, D. L. Wu, L. Liao, W. S. Olson, D. O. Barahona, Remote Sensing (Journal), Volume 14, Issue 15, Jul. 2022.

"ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles," X. Yuan, L. Ding, L. Zhang, X. Li and D. O. Wu, IEEE Transactions on Emerging Topics in Computational Intelligence, Mar. 2022.

"Adversarial Email Generation Against Spam Detection Models Through Feature Perturbation," Q. Cheng, A. Xu, X. Li, L. Ding, IEEE International Conference on Assured Autonomy (ICAA), Mar. 2022.

"Defending against GAN-based DeepFake Attacks via Transformation-aware Adversarial Faces," C. Yang, L. Ding, Y. Chen, H. Li, the International Joint Conference on Neural Networks (IJCNN), Jul. 2021.

"Crafting Adversarial Email Content against Machine Learning Based Spam Email Detection," C. Wang, D. Zhang, S. Huang, X. Li, and L. Ding, in Proceedings of the 2021 International Symposium on Advanced Security on Software and Systems (ASSS '21) with AsiaCCS, Jun. 2021.

"Are Smart Home Devices Abandoning IPV Victims?" A. Alshehri, M. B. Salem, L. Ding, IEEE TrustCom/C4W, Dec. 2020. [Link]

"Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks," X. Yuan, L. Ding, M. B. Salem, X. Li, D. Wu, SecureComm, Oct. 2020. [Link]

"A Novel Architecture for Automatic Document Classification for Effective Security in Edge Computing Environments," L. Ding, M. B. Salem, IEEE/ACM Symposium on Edge Computing/EdgeSP, Oct. 2018. [Link]

Professional Services

Co-chair, the Sixth ACM/IEEE Workshop on Security and Privacy in Edge Computing, Dec 2023.

Co-chair, the Forth ACM/IEEE Workshop on Security and Privacy in Edge Computing, Dec 2021. [Link]

Co-chair, IEEE International Workshop on Quantum Communication and Quantum Cryptography, Oct. 2021. [Link]

Local Chair, the IEEE/ACM International Conference on Connected Health Applications, Systems, and Engineering Technologies (CHASE), Dec. 2021. [Link]

Publicity co-chair, the IEEE Conference on Communications and Network Security (CNS), Oct. 2021. [Link]

Co-chair, the Third ACM/IEEE Workshop on Security and Privacy in Edge Computing, Nov. 2020. [Link]

Orgnazier and Panel Moderator, Women-in-Computing Forum of the ACM/IEEE Symposium on Edge Computing, Nov. 2019. [Link]

Professional Presentations

"A machine learning approach to connecting TSI and HMI observations," Sun-Climate Symposium, October, 2023.

"Extending 2D cloud images into 3D clouds using CloudSat/CALIPOS data through machine learning," CloudSat/CALIPSO Science Team Meeting, October, 2023.

"Physics-informed neural networks for cloud structure and ice water path retrieval," invited talk, NASA GSFC AI Center of Excellence seminar, May, 2023.

"Trust Preservation in the age of AI," invited talk, Women in Hardware and Systems Security(WISE) workshop, Dec. 2020. [Link]

"Hype or hope? Machine learning based security analytics for web applications," L. Ding, X. Yuan, M. B. Salem, Annual Computer Security Applications Conference (ACSAC)/Case Studies, Dec. 2019. [Link]

"Automated REST API endpoint identification for security testing at scale: how machine learning accelerates security testing," L. Ding, J. Jacob, J. Chen, S. Pham, Blackhat Asia, Mar. 2019. [Link]


  • Open Positions: paid student research assistant positions are available in the areas of big data analytics and machine learning for AU undergraduate and AU graduate students. If you are interested, please email me your transcripts, resume, a brief description of your research interests (specifying your programming and analytical skills, and relevant projects). 

  • Congratulations to our labbers Lexie Rista (BS - Computer Science), Archibald Latham (BS - Computer Science), and Huong Doan (MS - Data Science) on being the recipients of the 31st annual Robyn Rafferty Mathias Student Research Conference Awards! (April 2021) [link]